Rewarding High-Quality Data via Influence Functions

By Adam Richardson, Aris Filos-Ratsikas, Boi Faltings. 2019

45 citations recorded by the source corpus.

We consider a crowdsourcing data acquisition scenario, such as federated learning, where a Center collects data points from a set of rational Agents, with the aim of training a model. For linear regression models, we show how a payment structure can be designed to incentivize the agents to provide high-quality data as early as possible, based on a characterization of the influence that data points have on the loss function of the model. Our contributions can be summarized as follows: (a) we prove theoretically that this scheme ensures truthful data reporting as a game-theoretic equilibrium and further demonstrate its robustness against mixtures of truthful and heuristic data reports, (b) we design a procedure according to which the influence computation can be efficiently approximated and processed sequentially in batches over time, (c) we develop a theory that allows correcting the difference between the influence and the overall change in loss and (d) we evaluate our approach on real datasets, confirming our theoretical findings.

Subfields: Artificial Intelligence, Computer Science Applications, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lingjuan Lyu
2. Han Yu
3. Qiang Yang
4. Yang Liu
5. Tianjian Chen
6. Dusist Niyato
7. Xingjun Ma
8. Jiong Jin

In Computer Science Applications

1. Qiang Yang
2. Dusist Niyato
3. Han Yu
4. Lingjuan Lyu
5. Yang Liu

In Management Science and Operations Research

1. Tianjian Chen
2. Qiang Yang
3. Dusist Niyato
4. Lingjuan Lyu
5. Yang Liu
6. Yitong Li

Citations in the corpus

• A Sustainable Incentive Scheme for Federated Learning (2020, background): “Similar to Richardson et al. , 10 ; 9 Jia et al. computes a Shap-ley value to split reward among data owners.”
• Towards Fair and Privacy-Preserving Federated Deep Models (2019, background): “A scheme which pays for marginal improvements brought about by model updates was proposed in [28].”